The Role of Computational Linguistics and Translation Studies in Advancing Multilingual Communication and Cultural Inclusivity Worldwide
DOI:
https://doi.org/10.70062/gllr.v1i3.189Keywords:
Computational Linguistics, Translation Studies, Multilingual communication, Machine translationAbstract
This study explores the role of computational linguistics and translation studies in strengthening multilingual communication and fostering cultural inclusivity in the era of globalization. The limited representation of minority languages in language technologies creates communication gaps and reduces linguistic equity. Using an experimental NLP-based approach, this research employs corpora of majority and minority languages and leverages transformer models such as BERT, mBART, T5, and GPT. The process includes training, fine-tuning, and translation quality evaluation through BLEU, METEOR, and human assessment. The results demonstrate significant improvements in machine translation performance for minority languages after applying transformer-based models. Furthermore, translation studies contribute substantially to ensuring the accuracy, contextual relevance, and cultural meaning of translations. These findings have practical implications for developing more equitable and inclusive global communication and serve as a foundation for international language policy. The study also recommends strengthening cross-disciplinary collaboration to enrich minority language corpora, mitigate technological bias, and open pathways for further research in NLP and translation studies.
References
Agustini, S., Tumbelaka, S., & Porong, J. V. (2024). Respon viabilitas dan vigor benih jagung pulut (Zea mays var. ceratina L.) yang mengalami penyimpanan terhadap pemberian ekstrak bawang merah. Jurnal MIPA, 13(2), 42–47. https://doi.org/10.35799/jm.v13i2.558
Allaithy, A. (2025). Translation studies in a rapidly changing world. Dragoman, 18, iii–vii. https://doi.org/10.63132/ati.2025.forewo.00116590
Asha, K. H., Jaiswal, A., & Anand, A. (2022). English to Hindi machine translation using sub-classed model. In Cognitive Science and Technology (pp. 729–738). https://doi.org/10.1007/978-981-19-2350-0_69
Baxter, R. N. (2021). Sidelining women in translation: The Galician literary sector as a case study. Perspectives: Studies in Translation Theory and Practice, 29(5), 691–705. https://doi.org/10.1080/0907676X.2020.1770817
Bielsa, E. (2005). Globalisation and translation: A theoretical approach. Language and Intercultural Communication, 5(2), 131–144. https://doi.org/10.1080/14708470508668889
Carl, M., Bangalore, S., & Schaeffer, M. (2016). Computational linguistics and translation studies: Methods and models. Benjamins Translation Library, 126, 225–244. https://doi.org/10.1075/btl.126.11car
Chesterman, A. (2019). Consilience or fragmentation in translation studies today? Slovo.ru: Baltic Accent, 10(1), 9–20. https://doi.org/10.5922/2225-5346-2019-1-1
Czulo, O., & Hansen-Schirra, S. (2017). Crossroads between contrastive linguistics, translation studies and machine translation: TC3-II. https://doi.org/10.5281/zenodo.1019701
Derczynski, L. R. A. (2017). Introduction. Studies in Computational Intelligence, 677, 1–8. https://doi.org/10.1007/978-3-319-47241-6_1
Ghaderi, F., & Scalbert Yücel, C. (2021). An état présent of the Kurdish literature in English translation. The Translator, 27(2), 150–166. https://doi.org/10.1080/13556509.2021.1872196
Goitom, M. (2020). Multilingual research: Reflections on translating qualitative data. British Journal of Social Work, 50(2), 548–564. https://doi.org/10.1093/bjsw/bcz162
Hoyte-West, A. (2021). Hand in hand or worlds apart? An overview of translation and education in the Upper Sorbian context. Translation Studies: Theory and Practice, 1(2), 5–15. https://doi.org/10.46991/TSTP/2021.1.2.005
Jiménez-Crespo, M. Á. (2014). Translation. In The Routledge Handbook of Hispanic Applied Linguistics (pp. 295–312). Routledge. https://doi.org/10.4324/9781315882727-28
Kri, R., & Sambyo, K. (2020). Phrase-based machine translation of Digaru-English. In Lecture Notes in Electrical Engineering (Vol. 686, pp. 983–992). https://doi.org/10.1007/978-981-15-7031-5_94
Li, Q., Ma, Z., Yang, S., Li, X., Ma, Z., & Wei, X. (2018). Reconstruct the scope, content and approaches of computational linguistics. In ACM International Conference Proceedings Series (pp. 77–81). https://doi.org/10.1145/3301551.3301590
Mariani, J. (2009). Language technology infrastructures in support to multilingualism. In Proceedings of the ACM International Conference (pp. 3–10). https://doi.org/10.1145/1667780.1667782
Martí, M. Á., & Taulé, M. (2014). Computational Hispanic linguistics. In The Routledge Handbook of Hispanic Applied Linguistics (pp. 350–370). Routledge. https://doi.org/10.4324/9781315882727-31
Meylaerts, R. (2009). Which translation policy for which linguistic minorities? Meta, 54(1), 7–21. https://doi.org/10.7202/029790ar
Moccozet, L., & Böckh, M. (2023). Digital DLC models as instruments for raising awareness and better understanding of current multilingualism in HEI. Multilingual Education, 45, 45–65. https://doi.org/10.1007/978-3-031-37027-4_3
Moccozet, L., Boeckh, N., & Aronin, L. (2021). Modelling socio-linguistic profiles in multilingual higher education institutions (HEI). In Proceedings of the 19th International Conference on Information Technology Based Higher Education and Training (ITHET 2021). https://doi.org/10.1109/ITHET50392.2021.9759794
Monson, C., Llitjós, A. F., Ambati, V., Levin, L., Lavie, A., Alvarez, A., Aranovich, R., Carbonell, J., Frederking, R., Peterson, E., & Probst, K. (2008). Linguistic structure and bilingual informants help induce machine translation of lesser-resourced languages. In Proceedings of the 6th International Conference on Language Resources and Evaluation (LREC 2008) (pp. 2854–2859).
Neumann, S., Hansen-Schirra, S., & Čulo, O. (2017). Annotation, exploitation and evaluation of parallel corpora: TC3 I. https://doi.org/10.5281/zenodo.283376
Patankar, S. N., & Devane, S. R. (2017). Issues in resolving word sense disambiguation for multilingual translation framework. In Proceedings of the International Conference on Intelligent Computing and Control Systems (ICICCS) (pp. 230–235). https://doi.org/10.1109/ICCONS.2017.8250716
Phadke, M. M., & Devane, S. R. (2017). Multilingual machine translation: An analytical study. In Proceedings of the International Conference on Intelligent Computing and Control Systems (ICICCS) (pp. 881–884). https://doi.org/10.1109/ICCONS.2017.8250590
Piller, I., Zhang, J., & Li, J. (2020). Linguistic diversity in a time of crisis: Language challenges of the COVID-19 pandemic. Multilingua, 39(5), 503–515. https://doi.org/10.1515/multi-2020-0136
Raman Deep, S., Nagar, S. S., Upendra, R. S., & Karthik, R. (2024). Design and development of user-friendly bi-lingual translation system employing machine translation base deep learning neural network framework based NLP. In Proceedings of AIMLA 2024. https://doi.org/10.1109/AIMLA59606.2024.10531504
Sidorov, G. (2019). Formalization in computational linguistics. SpringerBriefs in Computer Science. https://doi.org/10.1007/978-3-030-14771-6_1
Soria, C., Russo, I., Quochi, V., Hicks, D., Gurrutxaga, A., Sarhimaa, A., & Tuomisto, M. (2016). Fostering digital representation of EU regional and minority languages: The Digital Language Diversity Project. In Proceedings of the 10th International Conference on Language Resources and Evaluation (LREC 2016) (pp. 3256–3260).
Stein-Smith, K. (2021). Multilingualism for global solutions and a better world. Journal of Language Teaching and Research, 12(5), 671–677. https://doi.org/10.17507/jltr.1205.05
Tayal, M. A., Nakhate, S., Sinha, S., Ridhorkar, S. M., Chauhan, R., & Thakur, R. S. (2024). Language typology driven multi-lingual machine translation for Indian languages. AIP Conference Proceedings, 3214(1), 020032. https://doi.org/10.1063/5.0239853
Wang, W., Hu, J., Wei, H., Ubul, K., Shao, W., Bi, X., He, J., Li, Z., Ding, K., Jin, L., & Gao, L. (2024). Survey on text analysis and recognition for multiethnic scripts. Journal of Image and Graphics, 29(6), 1685–1713. https://doi.org/10.11834/jig.240015
